Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Muhammad Zeshan Afzal"'
Publikováno v:
IEEE Access, Vol 10, Pp 120781-120791 (2022)
Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide
Externí odkaz:
https://doaj.org/article/4cdc388f284b4efb88c90b9fb2cd5870
Autor:
Hafiza Sundus Fatima, Imtiaz ul Hassan, Shehzad Hasan, Muhammad Khurram, Didier Stricker, Muhammad Zeshan Afzal
Publikováno v:
Applied Sciences, Vol 13, Iss 6, p 3997 (2023)
Weed management is becoming increasingly important for sustainable crop production. Weeds cause an average yield loss of 11.5% billion in Pakistan, which is more than PKR 65 billion per year. A real-time laser weeding robot can increase the crop’s
Externí odkaz:
https://doaj.org/article/434cb3034ac34d18b32d5e275127f441
Autor:
Khurram Azeem Hashmi, Marcus Liwicki, Didier Stricker, Muhammad Adnan Afzal, Muhammad Ahtsham Afzal, Muhammad Zeshan Afzal
Publikováno v:
IEEE Access, Vol 9, Pp 87663-87685 (2021)
The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. Table detection and structural recogn
Externí odkaz:
https://doaj.org/article/53c041a60244406e99b1d162d7487cc1
Autor:
Khurram Azeem Hashmi, Didier Stricker, Marcus Liwicki, Muhammad Noman Afzal, Muhammad Zeshan Afzal
Publikováno v:
IEEE Access, Vol 9, Pp 113521-113534 (2021)
This paper presents the novel approach towards table structure recognition by leveraging the guided anchors. The concept differs from current state-of-the-art systems for table structure recognition that naively apply object detection methods. In con
Externí odkaz:
https://doaj.org/article/58a057f6652f44f2b5aa753c244a39ae
Autor:
Azmat Hussain, Hafiza Sundus Fatima, Syed Mohiuddin Zia, Shehzad Hasan, Muhammad Khurram, Didier Stricker, Muhammad Zeshan Afzal
Publikováno v:
Machines, Vol 11, Iss 2, p 287 (2023)
Weed management has become a highly labor-intensive activity, which is the reason for decreased yields and high costs. Moreover, the lack of skilled labor and weed-resistant herbicides severely impact the agriculture sector and food production, hence
Externí odkaz:
https://doaj.org/article/1654709ca8424bc4a136262d96e8b055
Autor:
Shishir Muralidhara, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Publikováno v:
Sensors, Vol 22, Iss 21, p 8583 (2022)
Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more challenging
Externí odkaz:
https://doaj.org/article/6bd2a2b025564961a015ea8db4f7f11d
Autor:
Sankalp Sinha, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Publikováno v:
Applied Sciences, Vol 12, Iss 20, p 10578 (2022)
In the age of deep learning, researchers have looked at domain adaptation under the pre-training and fine-tuning paradigm to leverage the gains in the natural image domain. These backbones and subsequent networks are designed for object detection in
Externí odkaz:
https://doaj.org/article/be4f88fa8a5841518cdb3ef2e5e1e063
Autor:
Mohammad Minouei, Khurram Azeem Hashmi, Mohammad Reza Soheili, Muhammad Zeshan Afzal, Didier Stricker
Publikováno v:
Applied Sciences, Vol 12, Iss 18, p 8969 (2022)
The growing amount of data demands methods that can gradually learn from new samples. However, it is not trivial to continually train a network. Retraining a network with new data usually results in a phenomenon called “catastrophic forgetting”.
Externí odkaz:
https://doaj.org/article/08e832142252498981048a3f3916bdec
Autor:
Muhammad Ahmed Ullah Khan, Danish Nazir, Alain Pagani, Hamam Mokayed, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Publikováno v:
Sensors, Vol 22, Iss 18, p 6969 (2022)
Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse dept
Externí odkaz:
https://doaj.org/article/32fa545fb49443c98471c4958d010b7e
Autor:
Tahira Shehzadi, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, Muhammad Zeshan Afzal
Publikováno v:
Applied Sciences, Vol 12, Iss 19, p 9398 (2022)
Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labele
Externí odkaz:
https://doaj.org/article/77f8a4a06d6e4cee82a99343d56b913b